# 加载训练集数据
train_data_file_path = '/home/aistudio/work/1214/train.csv'
pred_data_file_path = '/home/aistudio/work/1214/test.csv'
pred_output_file_path = '/home/aistudio/work/1214/pred/'

# 模型保存目录
model_check_point_dir = '/home/aistudio/work/1214/model/'

# 构造dict
dict = set(('<bos>', '<eos>', '<pad>', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '.', '-'))
max_len = 0

train_df = pd.read_csv(train_data_file_path, encoding='utf-8')
# df = pd.read_csv('/home/aistudio/work/1214/train.csv', encoding='utf-8')
train_smile_list = train_df['smiles'].tolist()
test_label_list = train_df['label'].tolist()

for elem in train_smile_list:
    if len(elem) > max_len:
        max_len = len(elem)
    for every_char in iter(elem):
        dict.add(every_char)

max_len = max_len + 2
dict = list(dict)
dict.sort()
dict_len = len(dict)
index_of_bos = dict.index('<bos>')
index_of_eos = dict.index('<eos>')
index_of_pad = dict.index('<pad>')

print(max_len)
print(dict)

# 加载训练集数据
pred_df = pd.read_csv(pred_data_file_path, encoding='utf-8')
pred_smile_list = pred_df['smiles'].tolist()

# 压缩参数
zip_file_path = '/home/aistudio/work/1214/model.zip'